Fall 2022

Quantifying Uncertainty: Stochastic, Adversarial, and Beyond

Monday, Sep 12, 2022 to Friday, Sep 16, 2022 

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Thodoris Lykouris (MIT; chair), Laura Doval (Columbia University), Kevin Jamieson (University of Washington)

The workshop will explore online decision-making under different modeling assumptions on the reward structure. The two classical approaches for that consist of the setting where rewards are stochastic from a distribution and the one where they are adversarially selected. We will discuss different hybrid models to go between these extremes (data-dependent algorithms that adapt to “easy data”, model-predictive methods, ML-augmented algorithms, etc). We will also consider settings where the rewards come from agents with particular behavioral or choice models and how the algorithms need to change to adapt to that.

Registration is required to attend this workshop. Space may be limited, and you are advised to register early.  To submit your name for consideration, please register and await confirmation of your acceptance before booking your travel.

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Invited Participants: 
Anish Agarwal (MIT), Yossi Azar (Tel-Aviv University), Arjada Bardhi (Duke University), Steve Callander (Stanford University), Modibo Camara (Northwestern University), Victor Gabillon (Queensland University of Technology), Kyra (Jingyi) Gan (Harvard University), Ravi Kumar (Google), Hannah Li (Stanford), Ilan Lobel ((None)), Benjamin Moseley (Carnegie Mellon University), Vidya Muthukumar (Georgia Institute of Technology), Marco Ottaviani (Bocconi University), Chara Podimata (UC Berkeley), Aleksandrs Slivkins (Microsoft Research), Wen Sun (Cornell University), Csaba Szepesvári (University of Alberta, Google DeepMind), Panos Toulis (University of Chicago, Booth School of Business), Can Urgun (Princeton University), Haifeng Xu (University of Chicago), Julian Zimmert (Google Research)